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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Particulate Matter Forecasting Using Different Deep Neural Network Topologies and Wavelets for Feature Augmentation
Stephanie Lima Jorge Galvão, Júnia Cristina Ortiz Matos, Yasmin Kaore Lago Kitagawa, et al.
Atmosphere (2022) Vol. 13, Iss. 9, pp. 1451-1451
Open Access | Times Cited: 11

Showing 11 citing articles:

A transformer-based deep neural network with wavelet transform for forecasting wind speed and wind energy
Erick Giovani Sperandio Nascimento, T. A. C. Melo, Davidson Martins Moreira
Energy (2023) Vol. 278, pp. 127678-127678
Open Access | Times Cited: 86
Artificial intelligence for geoscience: Progress, challenges and perspectives
Tianjie Zhao, Sheng Wang, Chaojun Ouyang, et al.
The Innovation (2024) Vol. 5, Iss. 5, pp. 100691-100691
Open Access | Times Cited: 59
A city-based PM2.5 forecasting framework using Spatially Attentive Cluster-based Graph Neural Network model
Subhojit Mandal, Mainak Thakur
Journal of Cleaner Production (2023) Vol. 405, pp. 137036-137036
Closed Access | Times Cited: 35
Comparative Analysis of Multiple Deep Learning Models for Forecasting Monthly Ambient PM2.5 Concentrations: A Case Study in Dezhou City, China
Zhenfang He, Qingchun Guo
Atmosphere (2024) Vol. 15, Iss. 12, pp. 1432-1432
Open Access | Times Cited: 15
AI-driven surrogate modelling for simulating hydrogen production via proton exchange membrane water electrolysers
Mohammad Abdul Baseer, Harjeet Singh, Prashant Kumar, et al.
International Journal of Hydrogen Energy (2025) Vol. 127, pp. 462-483
Closed Access
Comparative Analysis of Machine Learning Models for Predicting PM2.5 Concentrations Using Meteorological and Chemical Indicators
Muhammad Haseeb, Zainab Tahir, Syed Amer Mahmood, et al.
Journal of Atmospheric and Solar-Terrestrial Physics (2024) Vol. 263, pp. 106338-106338
Closed Access | Times Cited: 3
Particulate Matter (PM1, 2.5, 10) Concentration Prediction in Ship Exhaust Gas Plume through an Artificial Neural Network
Giedrius Šilas, Paulius Rapalis, Sergėjus Lebedevas
Journal of Marine Science and Engineering (2023) Vol. 11, Iss. 1, pp. 150-150
Open Access | Times Cited: 6
HDLP: air quality modeling with hybrid deep learning approaches and particle swam optimization
E. A. Osman, C. Banerjee, Ajeet Singh Poonia
Innovations in Systems and Software Engineering (2024) Vol. 20, Iss. 3, pp. 287-299
Closed Access | Times Cited: 1
Shipping Emissions and Air Pollution: Latest Methodological Developments and Applications
Yuanqing Zhu, Long Liu
Atmosphere (2023) Vol. 14, Iss. 8, pp. 1312-1312
Open Access | Times Cited: 1
Improved $$NO_2$$ Prediction Using Machine Learning Algorithms
Chukwuemeka Jaja-Wachuku, Lorenzo Garbagna, Lakshmi Babu Saheer, et al.
IFIP advances in information and communication technology (2024), pp. 215-225
Closed Access
Application of Discrete Wavelet Transform and Tree-Based Ensemble Machine Learning for Modeling of Particulate Matter Concentrations
Maya Stoimenova-Minova, Snezhana Gocheva-Ilieva, Atanas Ivanov
Springer proceedings in mathematics & statistics (2023), pp. 171-183
Closed Access
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